This paper introduces Object-based Set Similarity (OSS), a novel metric designed to address computational costs and unreliable evaluations in active learning for real-world object detection. OSS quantifies active learning method effectiveness without requiring extensive detector training and improves evaluation robustness by identifying representative validation sets.